Free Courses to Learn ML
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5 Free University Courses to Learn Machine Learning

Are you eager to learn machine learning from top-notch resources? Explore these free machine learning courses offered by some of the world’s leading universities.

If you’re aiming for a career in data, mastering machine learning is crucial. While data analysis helps you understand past data to answer business questions, machine learning enables you to build models that predict future trends based on available data.

To help you get started with machine learning, we’ve curated a list of free courses from prestigious institutions like MIT, Harvard, Stanford, and the University of Michigan. I recommend reviewing the course contents to determine what aligns best with your learning goals. Based on your interests, you can choose one or more of these courses to pursue.

Let’s dive in!

1. Introduction to Machine Learning – MIT

Introduction to Machine Learning by MIT delves deeply into a wide range of ML topics. You can access the course materials, including exercises and practice labs, for free on MIT Open Learning Library.

This course covers everything from the basics of machine learning to advanced topics like ConvNets and recommender systems.

Topics Covered:

  • Linear Classifiers
  • Perceptrons
  • Margin Maximization
  • Regression
  • Neural Networks
  • Convolutional Neural Networks
  • State Machines and Markov Decision Processes
  • Reinforcement Learning
  • Recommender Systems
  • Decision Trees and Nearest Neighbors

Link: Introduction to Machine Learning

2. Data Science: Machine Learning – Harvard

Data Science: Machine Learning from Harvard focuses on practical applications such as building movie recommendation systems. This course is part of the Data Science Professional Certificate Program.

Topics Covered:

  • Basics of Machine Learning
  • Cross-Validation and Overfitting
  • Machine Learning Algorithms
  • Recommendation Systems
  • Regularization

Link: Data Science: Machine Learning

3. Applied Machine Learning in Python – University of Michigan

Applied Machine Learning in Python offered by the University of Michigan on Coursera provides a comprehensive introduction to machine learning with a focus on practical implementation using scikit-learn.

You can sign up for free on Coursera and access the course contents for free in audit mode.

Topics Covered:

  • Introduction to Machine Learning and scikit-learn
  • Linear Regression
  • Linear Classifiers
  • Decision Trees
  • Model Evaluation and Selection
  • Naive Bayes, Random Forest, Gradient Boosting
  • Neural Networks
  • Unsupervised Learning

This course is part of the Applied Data Science with Python specialization.

Link: Applied Machine Learning in Python

4. Machine Learning – Stanford

CS229: Machine Learning at Stanford is one of the most highly recommended machine learning courses. It explores various learning paradigms including supervised, unsupervised, and reinforcement learning. The course also covers techniques like regularization to help build models that generalize well.

Topics Covered:

  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Generalization and Regularization
  • Reinforcement Learning and Control

Link: Machine Learning

5. Statistical Learning with Python – Stanford

Statistical Learning with Python covers the content of the ISL with Python book, providing essential tools for data science and statistical modeling. This course, paired with the book, is a great resource for learning fundamental techniques in statistical learning.

Topics Covered:

  • Linear Regression
  • Classification
  • Resampling
  • Linear Model Selection
  • Tree-Based Methods
  • Unsupervised Learning
  • Deep Learning

Link: Statistical Learning with Python

Wrapping Up

I hope you find this list of free machine learning courses from top universities helpful. Whether you aim to become a machine learning engineer or want to delve into machine learning research, these courses will provide you with a solid foundation. Happy learning! 

About the Author: Kanishk Vats is a developer and technical writer, passionate about the intersection of math, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He enjoys reading, writing, coding, and coffee. Currently, he’s focused on learning and sharing knowledge with the developer community by authoring tutorials, guides, opinion pieces, and more.